A New Natural Barcoding Method Enables Personalized Medicine

The method is capable of testing many people simultaneously, which not only provides more information, but also saves costs and significant time.

A genome is the genetic material of an organism that contains DNA. The human genome carries approximately 10 million genetic variations known as SNPs (single nucleotide polymorphisms). These variations correspond to a difference of a single letter in the genetic code.

A genetic code is a set of instruction that represents encoded information, which determines a protein’s amino acid sequence. Each of us has a unique, stable SNPs. These unique patterns of SNPs are inherited (from biological parents) and barely mutated. So you can say it’s some sort of ‘natural barcode’ that can be used to uniquely locate cells of any person.

We’ve been studying SNPs for almost 20 years and it’s well known to us, but we haven’t been able to unlock their features as barcodes. SNPs are scattered (in a small quantity) throughout the genome – about 1 SNP appears in a thousand base pairs. This means that any single SNP is capable of differentiating only 2 people.

The existing technology can’t read more than a thousand base pairs sequentially. Therefore, it’s quite impractical to assign each sequencing-read to any specific individual as per SNPs.

Now, researchers at Harvard University have come up with a new genetic analysis approach that utilizes these natural barcodes to build simpler, cheaper and faster technique to track how cells in different people respond when they’re exposed to certain conditions. This way, a large volume of cells from different people can be examined for personalized medicine.

How Did They Do This?

It’s now becoming practical to carry out tests on cells from different people concurrently, thanks to the Big Data revolution. However, in order to keep track of which cells belong to which individual, it’s currently necessary to add a barcode (unique sequence of DNA) to each person’s cells. This is expensive and time consuming process.

However, by utilizing the unique SNP profiles of humans, the researchers were able to perform the same cell tracking without adding any label. The new technique merges the following components –

DNA extracted from different individual cells

Complete genome sequencing of extracted DNA

An algorithm (implemented in Java) for predicting the proportion of each person’s cells

SNP profiles could be used to trace the identities of cells across a large number of experiments, in which cells from different people are exposed to more than one condition, following further examination. Generally, these two conditions are ‘control’ and ‘experimental’ condition.

Workflow of how barcoding technique is used for testing cells from different donors

The algorithm does the prediction, both before & after the experiment. Then it compares the outcomes to discover which cells are changed after being exposed to external conditions. This way, it can tell whether any particular person’s cells have a genetic benefit.

Testing

To test this technique, the team simulated a bunch of cells, different samples, number of SNPs as well as sequencing read-depth. Over multiple iterations, their algorithm converged to a specific proportion for each person’s SNP, which is equivalent to the simulated proportions.

More specifically, the algorithm precisely predicted the proportions of a thousand different people by examining half a million SNPs. It can process even more samples of multiple cell lines, in case if the sequencing depth was expanded.

They also tested the algorithm on human B cell (type of white blood cell), and discovered that it precisely forecasted the people’s proportion within 50 distinct cell lines.

Moreover, this method could be applied to various other experiments. For instance, one can experiment with cancer drug against multiple cells from different individuals, and observe whether any person’s cell countered to this drug, and eventually use the same drug in treatment as personalized medicine.

Multiple cancer cells could be tested against drug effects. The method is capable of testing many people simultaneously, which not only provides more information, but also saves costs and significant time.